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dc.contributor.authorLiang, L
dc.contributor.authorKrishnamurthi, R
dc.contributor.authorKasabov, N
dc.contributor.authorFeigin, V
dc.date.accessioned2014-03-21T00:39:32Z
dc.date.available2014-03-21T00:39:32Z
dc.date.copyright2014
dc.date.issued2014-03-21
dc.identifier.citationSpringer Handbook of Bio-/Neuroinformatics (2014), pp 993-1001
dc.identifier.isbn978-3-642-30573-3
dc.identifier.urihttp://hdl.handle.net/10292/6988
dc.description.abstractStroke is a major cause of disability and mortality in most economically developed countries. It is the second leading cause of death worldwide (after cancer and heart disease) [55.1, 2] and a major cause of disability in adults in developed countries [55.3]. Personalized modeling is an emerging effective computational approach, which has been applied to various disciplines, such as in personalized drug design, ecology, business, and crime prevention; it has recently become more prominent in biomedical applications. Biomedical data on stroke risk factors and prognostic data are available in a large volume, but the data are complex and often difficult to apply to a specific person. Individualizing stroke risk prediction and prognosis will allow patients to focus on risk factors specific to them, thereby reducing their stroke risk and managing stroke outcomes more effectively. This chapter reviews various methods–conventional statistical methods and computational intelligent modeling methods for predicting risk and outcome of stroke.
dc.publisherSpringer Verlag
dc.relation.ispartofseriesedited by Kasabov, N
dc.relation.urihttp://link.springer.com/chapter/10.1007%2F978-3-642-30574-0_55
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.titleInformation methods for predicting risk and outcome of stroke
dc.typeChapter in Book
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1007/978-3-642-30574-0
aut.publication.placeBerlin
aut.relation.chapternumber55
pubs.elements-id149070


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